Abstract
This paper deals with isolation of failed components in the system. Each component can be affected in a random way by failures. The state of a component or a subsystem is detected using tests. The goal of this paper is to exploit the techniques of built-in tests and available knowledge to generate the sequence of tests required to locate quickly all the components responsible for system failure. We consider an operative system according to a series structure for which we know test cost and the conditional probability that a component is responsible for the failure. The various diagnosis strategies are analyzed. The treated algorithms relay on system probabilistic analysis.
Similar content being viewed by others
References
Canfield, R. V., & Nachlas, J. A. (1991). Diagnostic-strategy selection for a series-system. IEEE Transactions on Reliability, 40(2), 165.
Dugan, J. B., Bavuso, S. J., & Boyd, M. A. (1992). Dynamic fault tree models for fault tolerant computer systems. IEEE Transactions on Reliability, 41(3), 363–377.
Gao, J. (1997). Surveillance et diagnostic de pannes, Thèse de doctorat, département de génie mécanique, université Laval, Québec.
Gao, R. X., & Suryavanshi, A. (2002). Diagnosis from within the system. IEEE Instrumentation and Measurement Magazine, 5(3), 43–47.
Garey, R., & Graham, R. L. (1974). Performance bounds on the splitting algorithm for binary testing. Acta Informatica, 3(4), 347–354.
Giraud, L. (1995). Génération automatique de séquences de tests pour la détection rapide de pannes, département de génie mécanique, université Laval, Québec.
Gluss, B. (1959). Optimum policy for detecting a fault in a complex system. Operations Research, 7(4), 468–477.
Gluss, B., & Firstman, S. T. (1960). Optimum search routine for automatic fault location. Operations research, 8(4), 511–522.
Hao, X. C., Wu, J. Z., Chien, C. F., & Gen, M. (2014). The cooperative estimation of distribution algorithm: A novel approach for semiconductor final test scheduling problems. Journal of Intelligent Manufacturing, 25(5), 867–879.
Hou, T. H., Liu, W., & Lin, L. (2003). Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets. Journal of Intelligent Manufacturing, 14(2), 239–253.
Hsu, C.-C., & Chen, M.-S. (2014). Intelligent maintenance prediction system for LED wafer testing machine. Journal of Intelligent Manufacturing, 2014, 1–8.
Iyengar, V., & Chakrabarty, K. (2002). System-on-a-chip test scheduling with precedence relationships, preemption, and power constraints. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 21(9), 1088–1094.
Joshi, P., Imadabathuni, M., He, D., Al-Kateb, M., & Bechhoefer, E. (2012). Application of the condition based maintenance checking system for aircrafts. Journal of Intelligent Manufacturing, 23(2), 277–288.
Lim, K. S., Lee, C., Park, J. H., & Lee, S. J. (2014). Test-driven forensic analysis of satellite automotive navigation systems. Journal of Intelligent Manufacturing, 25(2), 329–338.
Nachlas, J. A., Loney, S. R., & Binney, B. A. (1990). Diagnostic-strategy selection for series-system. IEEE Transactions on Reliability, 39(3), 273–280.
Palshikar, G. K. (2002). Temporal fault trees. Information and Software Technology, 44(3), 137–150.
Pattipati, Krishna R., Ded, S., Dontamsetty, M., & Maitra, A., (1991). START: system testability analysis and research, AUTOTESTCON ’90. In IEEE systems readiness technology conference. ’Advancing mission accomplishment’, conference record (pp. 395–402, 17–21).
Pattipati, K. R., & Alexandridis, M. G. (1990). Application of heuristic search and information theory to sequential fault diagnosis. IEEE Transactions on Systems, Man and Cybernetics, 20(4), 872–887.
Pattipati, K. R., & Fang, T. (2003). Rollout strategies for sequential fault diagnosis. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 33(1), 86–99.
Shu-Guang, H., Zhen, H., & Gang, A. W. (2013). Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques. Journal of Intelligent Manufacturing, 24(1), 25–34.
Simeu-Abazi, Z., Di Mascolo, M., & Knotek, M. (2010). Fault diagnosis for discrete event systems: Modeling and verification. Reliability Engineering and system safety, 95(4), 369–378.
Simeu-Abazi, Z., & Lefebvre, A. (2011). A methodology of alarm filtering by using dynamic fault tree. Reliability Engineering and System Safety, 96(2), 257–266.
Singh, M. G. (1987). Fault detection and reliability: Knowledge based and other approaches, ser. International series on systems and control. New York: Pergamon Press.
Varshney, P. K., Hartman, C. R. P., & De Faria, J. M. (1974). Application of information theory to sequential fault diagnosis. IEEE Transactions on Computers, C–31(2), 164–170.
Varshney, P. K., & Hartman, C. R. P. (1984). Sequential fault diagnosis of modular system. IEEE Transactions on Computers, C–33(2), 194–197.
Vesely, W. E., Stamatelatos, M., Dugan, J. B., Fragola, J., Minarick, J., & Railsback, J. (2002). Fault tree handbook with aerospace applications. NASA Office of Safety and Mission Assurance.
Wang, S., Wang, L., Liu, M., & Xu, Y. (2013). A hybrid estimation of distribution algorithm for the semiconductor final testing scheduling problem. Journal of Intelligent Manufacturing. doi:10.1007/s10845-013-0821-3.
Wu, J.-Z., Hao, X.-C., Chien, C.-F., & Gen, M. (2012). A novel bi-vector encoding genetic algorithm for the simultaneous multiple resources scheduling problem. Journal of Intelligent Manufacturing, 23(6), 2255–2270.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Aït-Kadi, D., Simeu-Abazi, Z. & Arous, A. Fault isolation by test scheduling for embedded systems using a probabilistic approach. J Intell Manuf 29, 641–649 (2018). https://doi.org/10.1007/s10845-015-1088-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-015-1088-7